The journey of Generative AI has just begun, and we’re already witnessing its incredible power and diverse use cases. The impact so far is just a fraction and the overwhelming response hints at its immense potential.
Did you know that every day we generate more than 2.5 quintillion bytes of data? Every day we create approximately 328.77 million terabytes of data. This is equivalent to 2.5 quintillion bytes of data. 2.5 followed by 18 zeros.
Let me put this another way to give you all a different perspective. That is equivalent to Streaming movies
~4 Years with 20 GB per second speed. This would give you an estimate of the time required in years under ideal conditions.
TL;DR
I will calculate this and show you how much time streaming this data with 4G requires.
(check on this my LinkedIn post)
Introduction
Data Engineering is a field where we learn how to derive insight from data. Data Engineering is the process of designing a scalable system that collects and analyzes large and complex datasets from different source systems. Let’s dive into how these systems help businesses use data in useful ways.
The data engineering paradigm is huge There are 3 major Categories of Data Engineering. As we know we produce huge amounts of data. But the mind-boggling part is we only utilize tiny partitions for analyses.
Now imagine how big the Data engineering landscape is here we are talking quintillions of data every day.
Let’s dive deep into it and try to understand the Data Engineering landscape for 2024.
Key Components of Data Engineering
As we all know growing data requires attention. every organization produces and stores data. But the data sit in silos. Our job as Data engineer we have to enrich these silos of data and put them into a unified view and derive analysis out of it.
Data Collection and Integration
Data collection and integration are very important steps of the Data Engineering process. Everyone should have at least a basic understanding of the type of source data comes from.
There are two types of Data.
- Structured Data
- Unstructured Data
RDBMS systems like MySql, Oracle, Postgres DB, etc.
NoSQL systems like MongoDB, Cassandra, etc.
File-based storage like Amazon S3, Azure ADLS, Google GCS, FTP, SFTP, etc.
API-based systems like Marketing apps, CRM apps, Analytics applications, Sales and support systems, etc.
Unstructured data is growing, now we have just been able to analyze 18% of overall data which is Structured in nature. ~80 to 85% of data is in an unstructured format.
Till now we have seen a lot of players doing ETL or ELT on Structure data. Extract structure data from the source system then transform the load into the destination source.
Unstructured data has many formats.
Plain Text like PDF files, website content, word documents, and many more.
Audio files like music, podcasts, video files, and Images nowadays these types are getting popular and contain a lot of valuable information.
Integrating these data is very important to get more insight from the data. Now the biggest challenge comes while collecting these data from different sources.
This is a white space and very few companies are working towards unstructured data.
With the recent advancements in AI, we saw an opportunity to help businesses get hassle-free actionable insights. Now we can process and integrate structured and unstructured data to get better insight into the business.
What is Data Integration?
Unifying data into one place.
In the business world, data integration combines information from various sources, like customer databases, sales figures, and social media activity.
Imagine a company with customer data scattered across different systems: a CRM stores contact information, a sales app tracks purchases, and a website logs browsing activity. Each system holds valuable data, but it’s all isolated. Data integration acts like a bridge, bringing this data together to create a unified view.
Example: Building a 360-degree Customer View
By integrating data from these systems, the company can create a “360-degree view” of each customer. This view combines contact details, purchase history, and browsing behavior, providing a more complete understanding of customer preferences and needs. This allows for targeted marketing campaigns, improved customer service, and ultimately, increased customer satisfaction.
What is Data Transformation?
In data engineering, data transformation acts as the translator, cleaning, organizing, and formatting data from various sources into a consistent language for analysis.
Data integration brings the customer data together, but it might not be in a usable format. Data transformation takes that data and cleans it, organizes it, and converts it into a consistent format.
For example, addresses might be stored differently across systems (e.g., “St.” vs “Street”). Data transformation would standardize these formats. It might also convert currencies, fill in missing information, and ensure everything is ready for analysis. This clean and consistent data allows companies to extract valuable insights from their unified customer view.
Current State of Data Engineering
The data engineering landscape is booming! The ever-increasing volume and complexity of data require skilled professionals to build, maintain, and manage data infrastructure. This has led to a surge in demand for data engineers.
While the core responsibilities of data engineers remain crucial (building, maintaining, and managing data infrastructure), AI is rapidly transforming the data engineering landscape in 2024. Here are some key examples.
- Automating Repetitive Tasks
- Anomaly Detection and Data Quality Checks
- Data Lineage Tracking and Optimization
- Self-Healing Data Pipelines
- Democratizing Data Engineering
These are very wasteful processes that will cover every section in different blogs.
Evolving Skillset:
The traditional data engineer skillset is no longer enough. Today’s data engineers need to be well-versed in a broader range of technologies, including:
- Cloud platforms (AWS, Azure, GCP)
- Big Data frameworks (Spark, Hadoop)
- Data warehousing and databases (SQL, NoSQL)
- Programming languages (Python, Java, Scala)
- Machine Learning and AI fundamentals
- Data pipelines and orchestration tools (Airflow, Luigi)
- Version control systems (Git)
Polyglot Persistence:
Gone are the days when a single database technology could handle all data needs. The concept of “polyglot persistence” emphasizes the use of multiple database technologies, each chosen for its specific strengths.
For example, a data engineer might use a relational database (like MySQL) for structured data, a NoSQL database (like Cassandra) for highly scalable data, and a data lake (like AWS S3) for storing raw, unstructured data.
Rise of Multi-Model Databases:
To address the challenges of managing multiple databases, multi-model databases are gaining traction. These databases offer the flexibility to store and query different data types (structured, semi-structured, unstructured) within a single platform. This simplifies data management and streamlines data pipelines.
The Rise of Specialized Databases:
While “polyglot persistence” (using multiple database technologies) remains relevant, specialized databases like vector databases are gaining traction. These databases are designed for efficient storage and retrieval of high-dimensional data, particularly useful for applications like image recognition or recommendation systems.
How AI is Reshaping the Data Engineering Landscape?
The data engineering landscape is booming, fueled by the ever-increasing volume and complexity of data. This has led to a surge in demand for skilled professionals, but the traditional skillset is no longer enough.
- From Manual to Machine: Data pipelines often involve repetitive data transformations. AI can learn these patterns and automate the transformation process, improving efficiency and reducing human error.
- Data Quality with AI: Data quality is paramount for accurate analysis. AI algorithms can continuously monitor data pipelines and identify anomalies or inconsistencies in real time. This proactive approach ensures clean data is used for downstream analytics, preventing issues before they arise.
- AI simplifies Data Lineage: Tracking the origin and transformation of data within complex pipelines can be challenging. AI can automatically track data lineage, making it easier to understand how data is used and identify potential bottlenecks for optimization.
- Democratizing Data Engineering with AI-powered Tools: AI is not just for data engineers! User-friendly AI tools can empower non-technical users to perform basic data-wrangling tasks. This frees up data engineers to focus on complex problems and fosters collaboration across teams, allowing data democratization within organizations.
By leveraging AI and embracing specialized databases, data engineers in 2024 can work smarter, not harder. This allows them to focus on higher-level tasks like designing data architectures, optimizing data pipelines, and integrating these solutions with advanced analytics tools.
Conclusion
In 2024, data engineering will be a dynamic field brimming with opportunities. By staying updated on the latest trends, from AI to vector databases, data engineers can position themselves as valuable assets in the data-driven world. The future belongs to those who can embrace change and leverage technology to unlock the true potential of data.
We encourage you to explore further! What other trends do you see shaping the future of data engineering? Share your thoughts in the comments below.
Footnotes:
Additional Reading
- What is Data Engineering
- Logistic Regression for Machine Learning
- Cost Function in Logistic Regression
- Maximum Likelihood Estimation (MLE) for Machine Learning
OK, that’s it, we are done now. If you have any questions or suggestions, please feel free to comment. I’ll come up with more Machine Learning and Data Engineering topics soon. Please also comment and subs if you like my work any suggestions are welcome and appreciated.